MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202641043649 A) filed by KKR & KSR Institute of Technology and Sciences, Guntur, Andhra Pradesh, on April 6, for 'a meta-learning and explainable ai based recommendation framework for item cold-start problem.'
Inventor(s) include Dr. S. Radhakrishnan; Ms. A. Thanmai; Ms. K. Pujitha; Ms. A. Amrutha; and Ms. Sk. Nageena.
The application for the patent was published on April 17, under issue no. 16/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to a recommendation framework designed to solve the item cold-start problem in recommender systems. Traditional recommendation techniques struggle to generate accurate suggestions for newly introduced items due to limited interaction data. The proposed system integrates a meta-learning approach to enable rapid adaptation using minimal user-item interactions. A Model-Agnostic Meta-Learning (MAML) algorithm is employed to learn a generalized initialization from prior tasks, allowing efficient fine-tuning for new items. Additionally, the framework incorporates an explainable artificial intelligence module based on SHapley Additive exPlanations (SHAP) to provide interpretable insights for each recommendation. The system improves prediction accuracy, adaptation speed, and transparency. This invention is suitable for deployment in e-commerce platforms, digital media services, and other real-time recommendation environments."
Disclaimer: Curated by HT Syndication.
